{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *一行亮，表示这一行所有的训练图片和这一个测试图片都相距很远；一列亮，表示这一列的所有测试图片和这一个训练图片相距很远.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 145 / 500 correct => accuracy: 0.290000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$1 2 3 4\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$这有点难解释，直觉，5肯定不可以\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 37.339859 seconds\n",
      "One loop version took 39.075453 seconds\n",
      "No loop version took 1.897531 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072)\n",
      "(5000,)\n",
      "5\n",
      "5\n",
      "{1: [0.263, 0.257, 0.264, 0.278, 0.266], 3: [0.257, 0.263, 0.273, 0.282, 0.27], 5: [0.265, 0.275, 0.295, 0.298, 0.284], 8: [0.272, 0.295, 0.284, 0.298, 0.29], 10: [0.272, 0.303, 0.289, 0.292, 0.285], 12: [0.271, 0.305, 0.285, 0.289, 0.281], 15: [0.26, 0.302, 0.292, 0.292, 0.285], 20: [0.268, 0.293, 0.291, 0.287, 0.286], 50: [0.273, 0.291, 0.274, 0.267, 0.273], 100: [0.261, 0.272, 0.267, 0.26, 0.267]}\n",
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.257000\n",
      "k = 3, accuracy = 0.263000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.282000\n",
      "k = 3, accuracy = 0.270000\n",
      "k = 5, accuracy = 0.265000\n",
      "k = 5, accuracy = 0.275000\n",
      "k = 5, accuracy = 0.295000\n",
      "k = 5, accuracy = 0.298000\n",
      "k = 5, accuracy = 0.284000\n",
      "k = 8, accuracy = 0.272000\n",
      "k = 8, accuracy = 0.295000\n",
      "k = 8, accuracy = 0.284000\n",
      "k = 8, accuracy = 0.298000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 10, accuracy = 0.272000\n",
      "k = 10, accuracy = 0.303000\n",
      "k = 10, accuracy = 0.289000\n",
      "k = 10, accuracy = 0.292000\n",
      "k = 10, accuracy = 0.285000\n",
      "k = 12, accuracy = 0.271000\n",
      "k = 12, accuracy = 0.305000\n",
      "k = 12, accuracy = 0.285000\n",
      "k = 12, accuracy = 0.289000\n",
      "k = 12, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.260000\n",
      "k = 15, accuracy = 0.302000\n",
      "k = 15, accuracy = 0.292000\n",
      "k = 15, accuracy = 0.292000\n",
      "k = 15, accuracy = 0.285000\n",
      "k = 20, accuracy = 0.268000\n",
      "k = 20, accuracy = 0.293000\n",
      "k = 20, accuracy = 0.291000\n",
      "k = 20, accuracy = 0.287000\n",
      "k = 20, accuracy = 0.286000\n",
      "k = 50, accuracy = 0.273000\n",
      "k = 50, accuracy = 0.291000\n",
      "k = 50, accuracy = 0.274000\n",
      "k = 50, accuracy = 0.267000\n",
      "k = 50, accuracy = 0.273000\n",
      "k = 100, accuracy = 0.261000\n",
      "k = 100, accuracy = 0.272000\n",
      "k = 100, accuracy = 0.267000\n",
      "k = 100, accuracy = 0.260000\n",
      "k = 100, accuracy = 0.267000\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "X_train_folds = np.array_split(X_train, num_folds, axis = 0)\n",
    "y_train_folds = np.array_split(y_train, num_folds, axis = 0)\n",
    "print(len(X_train_folds))\n",
    "print(len(y_train_folds))\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "for kk in k_choices:\n",
    "    # 现在X_train_folds，y_train_folds都是列表\n",
    "    accuracy = []\n",
    "    for fold in range(num_folds):\n",
    "        X_temp = copy.deepcopy(X_train_folds)\n",
    "        y_temp = copy.deepcopy(y_train_folds)\n",
    "        \n",
    "        X_val = X_temp.pop(fold)\n",
    "        X_tra = np.concatenate(X_temp)\n",
    "        y_val = y_temp.pop(fold)\n",
    "        y_tra = np.concatenate(y_temp)\n",
    "        \n",
    "        classifier.train(X_tra, y_tra)\n",
    "        y_val_pred = classifier.predict(X_val,k=kk,num_loops=0)\n",
    "        \n",
    "        num_correct = np.sum(y_val_pred == y_val)\n",
    "        accuracy.append(float(num_correct) / X_val.shape[0])\n",
    "    k_to_accuracies[kk] = accuracy\n",
    "\n",
    "print(k_to_accuracies)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 144 / 500 correct => accuracy: 0.288000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 4\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$ 因为KNN的复杂度为O(N),N为训练集大小\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
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